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Baxter robot beats humans at noughts and crosses by multitasking

Using deep learning, the humanoid robot figured out how to play noughts and crosses from scratch by perceiving its surroundings and acting appropriately

7 December 2016

Your turn, Baxter

Clement Olalainty

By Aviva Rutkin

YOUR move, human. This robot is preparing to deal with the world by learning to play noughts and crosses.

Also known as tic-tac-toe, the game requires players to take turns drawing Xs or Os on a grid in a race to get three of their markers in a row. It’s a simple affair compared with other games mastered by artificial intelligence in recent years, such as Go and Jeopardy. But teaching a physical robot to play is trickier.

Heriberto Cuayahuitl at the University of Lincoln in the UK and his colleagues saw the paper-and-pencil puzzle as an opportunity to train a humanoid robot in multiple skills at once using deep learning.

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The robot wasn’t preprogrammed to make the decisions or actions needed to win the game. To play successfully, it needed to figure out how to perceive its surroundings, understand verbal instructions and interact appropriately with its environment. Essentially, it had to use different senses to make a judgement about how it should behave and then act accordingly.

“For a robot to learn what to do and say, based on what was heard and seen, is not a trivial task,” says Cuayahuitl.

These skills aren’t just for fun. Any robot destined to work alongside humans in daily life will need to be able to take in different types of information and make appropriate choices based on what it learns.

The team worked with humanoid robot Baxter, developed by Rethink Robotics in Boston. They equipped Baxter with software and sensors so it could see its surroundings, recognise speech, move its head to follow the gaze of the other player and move its arm to draw its own noughts or crosses in the grid.

The robot could also serve up a handful of preprogrammed comments at appropriate moments, such as “I take this one” when it claimed a box on the grid, and “Yes, I won!”

“A robot must be able to make appropriate choices if it is to work alongside humans in daily life”

Seven humans took turns playing with Baxter, which always selected to play noughts over crosses when it started a round. Deep learning algorithms helped it improve its game, as it figured out how to better perceive and respond to the humans’ actions. In the end, it won or tied 98 per cent of the time.

The work is being presented this week at the conference on Neural Information Processing Systems in Barcelona, Spain.

Down the line, Cuayahuitl’s team thinks their system can help efficiently train interactive robots. Future versions of their experiment may attempt broader conversations with humans, or take on more complicated games. The team is also planning to teach the robot to take its opponents’ emotions into account, so instead of winning every time, it could aim to perform in a way that makes its opponent happiest.

“The idea is to endow robots with the ability to develop and/or improve their own behaviours over time,” says Cuayahuitl.